We compare with previous state-of-the-art pose estimation methods on the `ALL' branch and the `machine_annot (M)' branch of InterHand2.6M V1.0 dataset [1]}. MPJPE (mm) is adopted to evaluate the 3D hand pose estimation accuracy. For fair comparisons, the AIH dataset is only used to train HASM and HDRM for amodal segmentation and de-occlusion. No pose annotations in AIH are used to train the pose estimator (SHPE). Table REF  summarizes the experimental results.
